COREnets
Installation
First, if you haven’t done so, install {remotes}:
install.packages("remotes")Proceed to install {COREnets} from Github:
remotes::install_github("NPSCORELAB/COREnets")Using the Package to Access Data
{COREnets} contains a series of network data sets that can be
accessed using the get_data function:
library(COREnets)
drugnet <- COREnets::get_data("drugnet")
drugnet
#> PROTO_NET drugnet Nodes:293 Edges:337
#> Acquaintanceship
#> +-D-- E:337 N:212 NC:1
#>
#> Edge list:
#> from from_class to to_class edge_class
#> 1 person 2 person Acquaintanceship
#> 1 person 10 person Acquaintanceship
#> 2 person 1 person Acquaintanceship
#> 334 entries not printed.
#> + Edge attributes:
#>
#> Node list:
#> + Node attributes: name<chr> node_class<chr> Gender<chr> Ethnicity<chr> HasTie<chr> hr_ethnicity<chr> hr_gender<chr> hr_has_tie<chr>In order to look up the available data sets use the list_data_sources
function:
COREnets::list_data_sources()
#> [1] "anabaptists"
#> [2] "australian_embassy_bombing_2004"
#> [3] "bali_bombings_2002"
#> [4] "bali_bombings_2005"
#> [5] "christmas_eve_bombings_2000"
#> [6] "ciel"
#> [7] "cocaine_smuggling_acero"
#> [8] "cocaine_smuggling_jake"
#> [9] "cocaine_smuggling_juanes"
#> [10] "cocaine_smuggling_mambo"
#> [11] "drugnet"
#> [12] "fifa"
#> [13] "harry_potter_death_eaters"
#> [14] "harry_potter_dumbledores_army"
#> [15] "london_gang"
#> [16] "madrid_train_bombings_2004"
#> [17] "montreal_street_gangs"
#> [18] "noordin_139"
#> [19] "november17"
#> [20] "paul_revere"
#> [21] "siren"
#> [22] "southeast_asian_aggregate_attack_series_2005"
#> [23] "vivace_bombing_cell_2005"
#> [24] "zegota"Get a brief description of the data set:
COREnets::get_description("drugnet")
#> [1] "These data represent a network of drug users in Hartford. Ties are directed and represent acquaintanceship. The network is a result of two years of ethnographic observations of people's drug habits. "Each data object contains two main lists of information, the reference
and network lists:
names(drugnet)
#> [1] "reference" "network"
class(drugnet$reference)
#> [1] "list"
class(drugnet$network)
#> [1] "list"reference
The reference list contains the following fields of information on the
data set:
|
Field |
Type |
Definition |
|---|---|---|
|
title |
character |
A formal title for the dataset as presented by other databases or the author. |
|
name |
character |
An informal dataset label for internal use. |
|
tags |
character |
An internal classification assinged to the dataset. |
|
description |
character |
A brief definition of the dataset to include the type of data, collection, etc. |
|
abstract |
character |
A brief summary of the data and network context. |
|
codebook |
data.frame |
A data table used for gathering and storing relationships and their definitions. |
|
bibtex |
character |
The citation for the dataset in bibtex format. Some datasets may have mupltiple entries. |
|
paper_link |
character |
Hyperlink(s) to publications linked to the dataset. |
network
The network list contains all the relevant data to generate a
sociogram and conduct the analysis. However, because each data set is
slightly different, this list is segmented into three entries:
metadata: A list of lists each containing information on the different edge types contained in the edge list. The following list are included as individual nested items for each edge type sub-graph, each contain a variety of fields:
| Field | Type | Definition |
|---|---|---|
| edge_class | character | A string matching the name of an edge class in the codebook. |
| is_bimodal | logical | A logial denoting wheter or not the edge type yields a bipartite graph. |
| is_directed | logical | A logical denoting whether the network edges are directed or not. |
| is_dynamic | logical | A logical denoting whether the edges are dynamic or not. |
| is_weighted | logical | A logical denoting whether or not the edges are weighted. |
| has_isolates | logical | A logical which defines if the graph contains isolates or not. |
| has_loops | logical | A logical defining the presence or absence of self-loops. |
| edge_count | double | A number corresponding to the number of edges. |
| node_count | double | A number corresponding to the number of nodes. |
| node_classes | double | A number corresponding to the number of node classes included for each edge class. |
edges_table: Adata.framethat contains a minimum of two columns, one column of nodes acting as a vector source or starting point (from) and another column of nodes that are the target of the connection (to). In addition to thefromandtovariables the data include a class variable for each (from_classandto_class).
drugnet$network$edges_table %>%
glimpse()
#> Observations: 337
#> Variables: 5
#> $ from <chr> "1", "1", "2", "2", "3", "4", "4", "5", "6", "7", "7", "7"…
#> $ to <chr> "2", "10", "1", "10", "7", "7", "211", "134", "152", "3", …
#> $ from_class <chr> "person", "person", "person", "person", "person", "person"…
#> $ to_class <chr> "person", "person", "person", "person", "person", "person"…
#> $ edge_class <chr> "Acquaintanceship", "Acquaintanceship", "Acquaintanceship"…nodes_table: Adata.framecontain node non-relational characteristics. A unique identifier for each node in theedge_tableshould be present in thenamevariable. In addition, anode_classobservation is included for each node.
drugnet$network$nodes_table %>%
glimpse()
#> Observations: 293
#> Variables: 8
#> $ name <chr> "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11",…
#> $ node_class <chr> "people", "people", "people", "people", "people", "peopl…
#> $ Gender <dbl> 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2,…
#> $ Ethnicity <dbl> 1, 1, 1, 1, 3, 3, 1, 3, 1, 3, 3, 2, 2, 2, 2, 3, 3, 3, 3,…
#> $ HasTie <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
#> $ hr_ethnicity <chr> "White/Other", "White/Other", "White/Other", "White/Othe…
#> $ hr_gender <chr> "Male", "Male", "Male", "Female", "Male", "Male", "Male"…
#> $ hr_has_tie <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TR…Generating Graph Objects
Each network in the package contains the necessary edges and nodes
tables to generate network objects with {igraph} or
{network}. For instance:
core_as_igraph(drugnet)
#> IGRAPH 5c4819f DN-- 293 337 --
#> + attr: name (v/c), node_class (v/c), Gender (v/n), Ethnicity (v/n),
#> | HasTie (v/n), hr_ethnicity (v/c), hr_gender (v/c), hr_has_tie (v/l),
#> | from_class (e/c), to_class (e/c), edge_class (e/c)
#> + edges from 5c4819f (vertex names):
#> [1] 1 ->2 1 ->10 2 ->1 2 ->10 3 ->7 4 ->7 4 ->211 5 ->134 6 ->152
#> [10] 7 ->3 7 ->4 7 ->9 8 ->107 8 ->117 9 ->1 9 ->2 9 ->7 10->1
#> [19] 10->2 11->135 11->220 12->89 13->216 14->24 14->52 16->10 16->19
#> [28] 17->64 17->79 18->55 18->104 18->165 19->18 20->64 20->182 21->16
#> [37] 21->22 22->21 22->64 22->107 23->20 23->22 23->64 24->14 24->31
#> [46] 24->124 27->117 28->29 29->28 30->19 31->24 31->32 31->124 31->149
#> + ... omitted several edges
core_as_network(drugnet)
#> Network attributes:
#> vertices = 293
#> directed = TRUE
#> hyper = FALSE
#> loops = FALSE
#> multiple = FALSE
#> bipartite = FALSE
#> total edges= 337
#> missing edges= 0
#> non-missing edges= 337
#>
#> Vertex attribute names:
#> Ethnicity Gender HasTie hr_ethnicity hr_gender hr_has_tie node_class vertex.names
#>
#> Edge attribute names:
#> edge_class from_class to_class